#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_reduce_sum.h"

#define CHECK_ACL(x)                                                                  \
  do                                                                                  \
  {                                                                                   \
    aclError __ret = x;                                                               \
    if (__ret != ACL_ERROR_NONE)                                                      \
    {                                                                                 \
      std::cerr << __FILE__ << ":" << __LINE__ << " aclError:" << __ret << std::endl; \
    }                                                                                 \
  } while (0);

#define CHECK_RET(cond, return_expr) \
  do                                 \
  {                                  \
    if (!(cond))                     \
    {                                \
      return_expr;                   \
    }                                \
  } while (0)

#define LOG_PRINT(message, ...)     \
  do                                \
  {                                 \
    printf(message, ##__VA_ARGS__); \
  } while (0)

int64_t GetShapeSize(const std::vector<int64_t> &shape)
{
  int64_t shapeSize = 1;
  for (auto i : shape)
  {
    shapeSize *= i;
  }
  return shapeSize;
}

int Init(int32_t deviceId, aclrtStream *stream)
{
  // 固定写法，AscendCL初始化
  auto ret = aclInit(nullptr);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetDevice(deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
  ret = aclrtCreateStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
  return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
                    aclDataType dataType, aclTensor **tensor)
{
  auto size = GetShapeSize(shape) * sizeof(T);
  // 调用aclrtMalloc申请device侧内存
  auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
  // 调用aclrtMemcpy将host侧数据拷贝到device侧内存上
  ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

  // 计算连续tensor的strides
  std::vector<int64_t> strides(shape.size(), 1);
  for (int64_t i = shape.size() - 2; i >= 0; i--)
  {
    strides[i] = shape[i + 1] * strides[i + 1];
  }

  // 调用aclCreateTensor接口创建aclTensor
  *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                            shape.data(), shape.size(), *deviceAddr);
  return 0;
}

int main(int argc, char *argv[])
{
  int n = atoi(argv[1]);
  // 1. （固定写法）device/stream初始化，参考AscendCL对外接口列表
  // 根据自己的实际device填写deviceId
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init AscendCL failed. ERROR: %d\n", ret); return ret);

  // 2. 构造输入与输出，需要根据API的接口自定义构造
  std::vector<int64_t> selfShape = {n, n};
  std::vector<int64_t> outShape = {n};
  void *selfDeviceAddr = nullptr;
  void *outDeviceAddr = nullptr;
  aclTensor *self = nullptr;
  aclIntArray *dims = nullptr;
  aclTensor *out = nullptr;
  std::vector<float> selfHostData(n * n, 2.0f);
  std::vector<float> outHostData = {0};
  std::vector<int64_t> dimsData = {0};
  bool keepDims = false;
  auto dtype = aclDataType::ACL_FLOAT;
  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建dims aclIntArray
  dims = aclCreateIntArray(dimsData.data(), dimsData.size());
  CHECK_RET(dims != nullptr, return ret);
  // 3. 调用CANN算子库API，需要修改为具体的API名称
  uint64_t workspaceSize = 0;
  aclOpExecutor *executor;

  int warmup = 100;
  int num_repeat = 100;
  aclrtEvent start, stop;
  float temp_time = 0;
  float time = 0;
  CHECK_ACL(aclrtCreateEvent(&start));
  CHECK_ACL(aclrtCreateEvent(&stop));
  void *workspaceAddr;

  for (int i = 0; i < warmup; i++)
  {
    // 调用aclnnReduceSum第一段接口
    ret = aclnnReduceSumGetWorkspaceSize(self, dims, keepDims, dtype, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReduceSumGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    void *workspaceAddr = nullptr;
    if (workspaceSize > 0)
    {
      ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
      CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
    }
    // 调用aclnnReduceSum第二段接口
    ret = aclnnReduceSum(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReduceSum failed. ERROR: %d\n", ret); return ret);
  }

  for (int i = 0; i < num_repeat; ++i)
  {
    // 调用aclnnReduceSum第一段接口
    ret = aclnnReduceSumGetWorkspaceSize(self, dims, keepDims, dtype, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReduceSumGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    void *workspaceAddr = nullptr;
    if (workspaceSize > 0)
    {
      ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
      CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
    }
    // 调用aclnnReduceSum第二段接口
    CHECK_ACL(aclrtSynchronizeStream(stream));
    CHECK_ACL(aclrtRecordEvent(start, stream));
    ret = aclnnReduceSum(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReduceSum failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSynchronizeStream(stream);
    CHECK_ACL(aclrtRecordEvent(stop, stream));

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    CHECK_ACL(aclrtSynchronizeEvent(stop));
    CHECK_ACL(aclrtEventElapsedTime(&temp_time, start, stop));
    time += temp_time;
    // 4.（固定写法）同步等待任务执行结束
  }
  printf("The repeat time is : %d\n", num_repeat);
  printf("Vmul perf is %f GFLOPS\n", (float)(n * n) / (time / num_repeat * 1e-3) / 1e9);

  // 4. （固定写法）同步等待任务执行结束
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // 5. 获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改
  auto size = GetShapeSize(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr,
                    size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
  for (int64_t i = 0; i < 16; i++)
  {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }

  // 6. 释放aclTensor和aclIntArray，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyIntArray(dims);
  aclDestroyTensor(out);

  // 7. 释放device资源，需要根据具体API的接口定义修改
  aclrtFree(selfDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0)
  {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}